Quantifying uncertainty in European climate projections using combined performance-independence weighting

Brunner, Lukas; Lorenz, Ruth; Zumwald, Marius; Knutti, Reto

doi:10.1088/1748-9326/ab492f

Cited by 103 publications

(130 citation statements)

References 46 publications

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“…Constraining the model uncertainty in this way brings CMIP5 and CMIP6 into closer agreement, although differences remain that need to be understood. Regional and multi-variate weighting schemes show promise in aiding this effort Lorenz et al 2018;Brunner et al 2019). Improving the reliability of projections will thus remain a focal point of future climate research, with methods for robust uncertainty partitioning being an essential part of that effort.…”

Section: Discussionmentioning

confidence: 99%

Partitioning climate projection uncertainty with multiple Large Ensembles and CMIP5/6

Lehner¹,

Deser²,

Maher³

et al. 2020

Preprint

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130

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Abstract. Partitioning uncertainty in projections of future climate change into contributions from internal variability, model response uncertainty, and emissions scenarios has historically relied on making assumptions about forced changes in the mean and variability. With the advent of multiple Single-Model Initial-Condition Large Ensembles (SMILEs), these assumptions can be scrutinized, as they allow a more robust separation between sources of uncertainty. Here, the iconic framework from Hawkins and Sutton (2009) for uncertainty partitioning is revisited for temperature and precipitation projections using seven SMILEs and the Climate Model Intercomparison Projects CMIP5 and CMIP6 archives. The original approach is shown to work well at global scales (potential method error

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Section: Discussionmentioning

confidence: 99%

Partitioning climate projection uncertainty with multiple Large Ensembles and CMIP5/6

Lehner¹,

Deser²,

Maher³

et al. 2020

Preprint

Self Cite

130

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show abstract

“…Previous studies have typically used multi-model ensembles to constrain future projections and some in particular have used a "leaveone-out" perfect model approach to examine the effectiveness of these methods (e.g. Knutti et al, 2017;Brunner et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

“…However, this leave-one-out approach is often used to tune particular parameters in the methodology, such as the performance weighting parameter in Brunner et al (2019). The use of the leave-one-out approach to tune the method is certainly well justified.…”

Section: Discussionmentioning

confidence: 99%

“…Kettleborough et al, 2007). In a recent paper, Brunner et al (2019) applied a model weighting technique was applied to the climate projections from the CMIP5 models over the European region. The model weighting was found to constrain the large spread in the CMIP5 models and reduce the implied uncertainty in the multi-model projections of European climate over the coming decades.…”

Section: Introductionmentioning

confidence: 99%

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Calibrating large-ensemble European climate projections using observational data

O’Reilly¹,

Befort²,

Weisheimer³

2020

Preprint

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Abstract. This study examines methods of calibrating projections of future regional climate using large single model ensembles (the CESM Large Ensemble and MPI Grand Ensemble), applied over Europe. The three calibration methods tested here are more commonly used for initialised forecasts from weeks up to seasonal timescales. The calibration techniques are applied to ensemble climate projections, fitting seasonal ensemble data to observations over a reference period (1920–2016). The calibration methods were tested and verified using an imperfect model approach using the historical/RCP 8.5 simulations from the CMIP5 archive. All the calibration methods exhibit a similar performance, generally improving the out-of-sample projections in comparison to the uncalibrated (bias-corrected) ensemble. The calibration methods give results that are largely indistinguishable from one another, so the simplest of these methods, namely Homogeneous Gaussian Regression, is used for the subsequent analysis. An extension to this method – applying it to dynamically decomposed data (in which the underlying data is separated into dynamical and residual components) – is also tested. The verification indicates that this calibration method produces more reliable and accurate projections than the uncalibrated ensemble for future climate over Europe. The calibrated projections for temperature demonstrate a particular improvement, whereas the projections for changes in precipitation generally remain fairly unreliable. When the two large ensembles are calibrated using observational data, the climate projections for Europe are far more consistent between the two ensembles, with both projecting a reduction in warming but a general increase in the uncertainty of the projected changes.

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“…Although not a complete sample of structural and epistemic uncertainty, these ensembles are an important part of exploring and quantifying drivers of past and future change and evaluating the success of policy interventions, such as stratospheric-ozone recovery resulting from the Montreal Protocol and its amendments (Dhomse et al, 2018;WMO, 2018). Typically, analysis of an ensemble investigates the behaviour and characteristics of the multi-model mean and the inter-model variance (Solomon et al, 2007;Tebaldi and Knutti, 2007;Butchart et al, 2010), rather than accounting for individual model performance or lack of model independence (Knutti, 2010;Räisänen et al, 2010). Methods to address these shortcomings have been proposed for simulations of the physical climate (e.g.…”

Section: Introductionmentioning

confidence: 99%

Projecting ozone hole recovery using an ensemble of chemistry–climate models weighted by model performance and independence

et al. 2020

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Abstract. Calculating a multi-model mean, a commonly used method for ensemble averaging, assumes model independence and equal model skill. Sharing of model components amongst families of models and research centres, conflated by growing ensemble size, means model independence cannot be assumed and is hard to quantify. We present a methodology to produce a weighted-model ensemble projection, accounting for model performance and model independence. Model weights are calculated by comparing model hindcasts to a selection of metrics chosen for their physical relevance to the process or phenomena of interest. This weighting methodology is applied to the Chemistry–Climate Model Initiative (CCMI) ensemble to investigate Antarctic ozone depletion and subsequent recovery. The weighted mean projects an ozone recovery to 1980 levels, by 2056 with a 95 % confidence interval (2052–2060), 4 years earlier than the most recent study. Perfect-model testing and out-of-sample testing validate the results and show a greater projective skill than a standard multi-model mean. Interestingly, the construction of a weighted mean also provides insight into model performance and dependence between the models. This weighting methodology is robust to both model and metric choices and therefore has potential applications throughout the climate and chemistry–climate modelling communities.

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Quantifying uncertainty in European climate projections using combined performance-independence weighting

Cited by 103 publications

References 46 publications

Partitioning climate projection uncertainty with multiple Large Ensembles and CMIP5/6

Partitioning climate projection uncertainty with multiple Large Ensembles and CMIP5/6

Calibrating large-ensemble European climate projections using observational data

Projecting ozone hole recovery using an ensemble of chemistry–climate models weighted by model performance and independence

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